1. Technical Field
This invention relates generally to the field of data processing and, more particularly, to techniques for automatic analysis and determination of a desired parameter from measurement data.
2. Description of Related Art
Explanation for oil, gas, and water entails the measurement of subsurface characteristics and the evaluation of the obtained data to determine petrophysical properties of interest for the relevant formation or reservoir. A basic process of subterranean formation evaluation consists of disposing sensors within or near a formation of interest to make the measurements. In the oil and gas industry, sensors are routinely deployed subsurface to acquire the measurements. The obtained measurement data is compiled in a recording or “log.”
Well logs are measurements, typically with respect to depth, of selected physical parameters of subterranean formations. The logs can consist of measurement data pertaining to a limited region (e.g., solely within the borehole) or extending deep within the formation. Well logs are typically recorded by deploying various types of instruments equipped with sensors into a borehole traversing the formation, moving the instruments along the borehole, and recording the measurements made by the sensors. The sensors can be deployed subsurface by various means of conveyance as known in the art, including deployment at the end of an armored electrical cable (e.g., wireline, slickline), mounted on tools designed to obtain the measurements while the borhole is bearing drilled (LWD/MWD), mounted on production string tools/cabling, mounted on casing, mounted on buoys/sleds for underwater use, in the form of fiber-optic sensors, etc.
The first electrical log was recorded in 1927 in a well in a small oil field in Alsace, France. The log, a single graph of the electrical resistivity of the rock formations cut by the borehole, was recorded by the “station” method. The downhole measurement instrument (sonde) was stopped at periodic intervals in the borehole, measurements were made, and the calculated resistivity was hand-plotted on a graph.
Conventional logging tools are readily combinable. In other words, the sensor-equipped sondes of several tools can be connected to form one tool and thereby make many measurements and logs on a single descent into and/or ascent from the borehole. The measurement date are correlated to depth and position by conventional tracking systems, such as a calibrated wheel system used in wireline logging.
Basic logging measurements may contain large amounts of information. Improved telemetry technology has provided a tremendous increase in the data rate that can be handled by conventional logging systems. Subsurface measurement data may be transmitted to the surface via various conventional telemetry systems, including fluid pressure modulation systems, electrical cabling electromagnetic signals, acoustic signals, wired drill pipes, ect. The measurement data may also be stored in a recording device disposed in the logging instrument or elsewhere. These data are typically recorded with respect to depth/time. A record of the subsurface sensor position with respect to time is made and correlated to the time/measurement record to generate a conventional log of measurements with respect to subsurface location.
The processing of the obtained measurement data can be performed on at least three levels: subsurface in the tool, uphole at the well site (e.g., in a mobile logging track unit), and at a central computing center. Conventional tools are routinely designed so that the measurement data are processed downhole and the processed signal is transmitted to the surface. In many cases, however, it is desirable to bring measured raw data to the surface for recording and processing. The original data are this available for any further processing or display purposes and are permanently preserved for future use.
The raw measurement data obtained with the downhole sensors contains the parameter information needed to derive the desired subsurface property or characteristic. For example, the petrophysical parameters needed to evaluate a formation or reservoir include, among others, porosity, hydrocarbon saturation, thickness, area, permeability, reservoir geometry, formation temperature and pressure, lithology, borehole fluid types and level a etc. Unfortunately, few of these parameters can be measured directly. Instead, they must be derived or inferred from the measurement of other subsurface parameters. A large number of parameters can be measured. They include, among others, resistivity, bulk density, interval transit time, spontaneous potential, natural radioactivity, and the hydrogen content of the rock. Log interpretation is the process by which these measurable parameters are translated into the desired petrophysical parameters of porosity, hydrocarbon saturation, permeability, producibility, lithology etc.
Log interpretation techniques have progressed from the linear solutions of simple equations in the 1940s to today's mathematical inversions and neural networks. At the early stage, interpretation was a sequential process accomplished with charts and monograms, which became increasingly complicated as a wider range of measurements became available and as the effects of environmental and well conditions were recognized. Log interpretation is now open to many options and iterations.
In the late 1970s, the idea of treating log interpretation as a problem of mathematical inversion was introduced. Each measurement was associated to a response equation that could be expressed as a set of unknown formation volumes to be solved. In the 1980s, inversion methods were further developed and different and models were run simultaneously to allow for selection of the best model. Whatever method is selected, the main tasks of well log interpretation algorithms have remained the same—to translate the acquired measurements from the sensors to quantities of petrophysical interest.
Parameter selection has always been a key subject in log interpretation. Interpretation algorithms often focus on a single type of sensor and generally depend on more parameters than are measured by the sensors for which they were designed. Values for the additional parameters are input by the engineers or log analysis who run or play back the log. Due to a lack of knowledge, insufficient time at the wellsite, or other factors, incorrect input parameters can be entered into the interpretation algorithms. Log quality and interpretation can suffer as a result. Historically, input parameters used in interpretation algorithms have been determined by explicit values entered by the user, measurements provided by a specific tool, or by a user-set switch which selects between these options. An example is the borehole diameter, which can be used to correct neutron porosity logs.
The borehole diameter (also known as “borehole caliper”) can be drawn from several sources. The source used to process the log is typically selected by the user from an itemized list One component of that list is the bit size. The bit size is a number that is typically entered by the user or obtained by a direct measurement of the borehole as a function of depth. A technique for automatic determination of the borehole caliper is described in U.S. Pat. No. 6,725,162. Other automated interpretation techniques have been proposed to provide estimations of downhole characteristics in an effort to improve log interpretation. U.S. Pat. No. 4,403,290 describes a log interpretation technique that evaluates the reliability of the logging computations. U.S. Pat. No. 6,571,619 describes a technique whereby measurements from multiple sensors are combined and processed to obtain petrophysical quantities. The use of artificial neutral networks has also been implemented to convert logs into desired quantities. The networks are trained on wells where results are already known, and once trained, they are applied to other wells in which the same model applies.
In practice, the quality of subsurface logging results depends on what is gleaned from the raw log measurement data. A need remains for improved techniques to minimize the problem of parameter selection.